Assessment Instructions: In this exercise, we will work with some real$-$world Geospatial style data to answer some research questions. At the end of this exercise, you should be able to: Import data from an Excel spreadsheet into Python. Plot the provided data in a meaningful way that provides useful insights. Make conclusions about the relationship between different data sets. The goal of this assessment is to investigate whether mean house prices in Victoria control the level of crime in an area. A higher population density $($people per square km$)$ results in a higher number of crimes committed. The crime rate $($average crimes committed per $100,000$ people$)$ is higher in areas with low mean house prices. In answering these you need to download the relevant data $($available here$),$ design and write Python code, produce a pseudo$-$code $($produce flowcharts and map out your code$)$ for plotting data and then interpret the results to determine relationships between the data. Step $1$: Explore Data Download the data sets available here describing the year$-$wise mean house prices, crime, population and area statistics. Open up the files and have a look at the data so that you can understand the format and data types. You are provided with clean data that has been extracted from the original Excel sheet that has been downloaded from a reputed source. You are encouraged to look into the original Excel sheet as well for your reference. The code for converting the original files to the clean code is provided for your understanding. This exploration will give you an idea of data you can use for analysis and experimentation. Note that the availability of house prices, crime, population and area statistics are not covered for all the years. Map out the thought process and structure you will need in your Python code in order to achieve the desired outcomes. Step $2$: Design Your Experiment Start with a broad outline $($you can draw flowcharts$)$ of the steps as designed in the first part of the assessment, create a pseudo$-$code for your research, and then fill in the coding logic to produce a full pseudo$-$code. Remember that pseudo$-$code should contain a mixture of structural programming words $($like for in a loop$)$ and brief English $($rather than full coding or full English$).$ Before you begin turning the pseudo$-$code into functional Python code, be sure to save a copy of your pseudo$-$code that you will submit as part of the deliverables for this assessment. Remember what we did in the practicals and the assignments, and use your experience and understanding for the design of the experiments. In Practical $2,$ we did some plots of the variables to see what is the distribution of the two variables, but we did not quantify them. You can quantify the correlation using some in$-$built Python functions, such as Pandas Pearson Correlation $"$x$.$corr$($y$)$Links to an external site." $($See Week $9$ lectures, where Julie explains them in detail$).$ Further to that, you are also provided with a shapefile of the whole of Australia, which you can adapt to your need to do map plots if you think that can help your visualisation $($you used Geopandas libraries in Practical $3).$ Moreover, you are free to create any animation $($covered in Assignment $1)/$ scatter plots$/$ histograms$/$ bar plots $($or any other forms of plots using Matplotlib$),$ if it helps you in visualization and proving the hypotheses. You can propose your own experiments. Be creative, you are not restricted. Step $3$: Write Your Python Code Use your pseudo$-$code to produce a functional Python code that reads the relevant data files in Excel format and allows you to visualise the data in a meaningful way. The code should be well commented, and reference to the pseudo$-$code and the flowchart should be made where possible. Step $4$: Interpret The Data for Analysis Produce a written report using the visual outputs from your code to justify your findings. The visual outputs should be backed up by theory. For instance, if you are plotting trends or correlation figures, you are required to discuss the what is expected results, and whether your results follow them. You might also discuss anomalies that do not fall into the expected trends, and provide plausible explanations. Correlation is not causation! You have to justify and explain your hypotheses backed up by facts. Without the detailed analysis, no HD grades will be granted for this assignment. Although not mandatory, you are free to consult published scientific research articles in the area using resources like Google Scholar, or Library services. Deliverables: $1.$ Flowchart$/$ workflow of the research investigation that shows how information flows from data to insights. $2.$ The Jupyter Notebook file$($s$)$ containing the Python codes you produced with results and comments that explain what the code is doing. $3.$ A report containing your investigation results and insights using visual $($and potentia